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DATA XL - THE PRICING COMPANY
HOME
PRICE DESK
INSURANCE PRICING ROOM
LAZARUS
MIFID
OTHER PRICING STUFF
OUR BROTHERS AND SISTERS
INSURANCE VALUE CHAIN
THE REASON WE EXIST
BLOG
DROP US A LINE
More
  • HOME
  • PRICE DESK
  • INSURANCE PRICING ROOM
  • LAZARUS
  • MIFID
  • OTHER PRICING STUFF
  • OUR BROTHERS AND SISTERS
  • INSURANCE VALUE CHAIN
  • THE REASON WE EXIST
  • BLOG
  • DROP US A LINE
DATA XL - THE PRICING COMPANY
  • HOME
  • PRICE DESK
  • INSURANCE PRICING ROOM
  • LAZARUS
  • MIFID
  • OTHER PRICING STUFF
  • OUR BROTHERS AND SISTERS
  • INSURANCE VALUE CHAIN
  • THE REASON WE EXIST
  • BLOG
  • DROP US A LINE

GENERAL INSURANCE PRICING VALUE CHAIN

The products marked with a * are currently on sale

Defining the tariff*

Market based pricing*

Market based pricing*

Who (department)

Product development and management (includes actuarial dpt)


Why (benefits)

Determine forward cost

Create underwriting structure

Develop underwriting standards


How

GLM model


Market based pricing*

Market based pricing*

Market based pricing*

Who (department)

Underwriting

Commercial

Reinsurances


Why (benefits)

Adjust price

Access underwriting results

Win-back customers

Market DD

Heat of the market


How

Web Crawlers

Brokers network

Experimental design



Value Based pricing*

Market based pricing*

Underwriting new business (quotation & issuance)

Who (department)

Underwriting 


Why (benefits)

Maximize revenue with the client and competition perspectives

Win-back customers

Commercial DD

Marketing plans


How

WtP with experimental design

Market price

Tariff analysis

Underwriting new business (quotation & issuance)

Portfolio measuring: Assess underwriting results*

Underwriting new business (quotation & issuance)

Who (department)

Underwriting


Why (benefits)

Understand the product life cycle as applied to insurance

Client communication management


How

Estimate/quote (thought IT automated system activities)

Virtual brokerage*

Portfolio measuring: Assess underwriting results*

Portfolio measuring: Assess underwriting results*

Who (department)

Sales rep


Why (benefits)

Determine the best offer to difficult to understand segments (p.ex. SME)

Over promised control

Commercial efficient


How

Brute force with broker&user experience

Artificial intelligence

Portfolio measuring: Assess underwriting results*

Portfolio measuring: Assess underwriting results*

Portfolio measuring: Assess underwriting results*

Who (department)

Portfolio management/Underwriting 


Why (benefits)

Assess underwriting results

Develop a view of a portfolio as a whole, rather than a case-specific perspective


How

Brute force with broker&user experience

Artificial intelligence

Refocusing an ailing portfolio*

Overcharge supplier detection (fraud)*

Refocusing an ailing portfolio*

Who (department)

Portfolio management/Underwriting 


Why (benefits)

Boost underwriting results

Develop a view of a portfolio as a whole, rather than a case-specific perspective


How

Identify non-performing segments within a portfolio

Build statistical strategies to refocus an ailing insurance portfolio

Reserves

Overcharge supplier detection (fraud)*

Refocusing an ailing portfolio*

Who (department)

Actuarial 


Why (benefits)

Determine past cost

Predict results 

Present a true and fair view to stakeholders


How

Establish reserving processes and methodologies

Integrate data and results with claims and underwriting

Overcharge supplier detection (fraud)*

Overcharge supplier detection (fraud)*

Overcharge supplier detection (fraud)*

Who (department)

Claims


Why (benefits)

 Fraud detection with a staistical score 


How

Improve overcharge detection and optimize sample selection with our powerful supplier screening tool

*

Our Main Approach Is Based In the Benford's Law

A statistical law – Benford’s Law (BL) – states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small:

Usually, the digit 1 appears as the leading significant digit about 30% of the time, while 9 appears as the leading significant digit less than 5% of the time. 

If the digits were distributed uniformly, they would each occur about 11.1% of the time. BL also makes predictions about the distribution of second digits, third digits, digit combinations, and so on. 


We apply this rule to the insurance claims dataset to find strange digit patterns and narrow the list of possible anomalous items, making the entire audit process more manageable. We also pursue other digit patterns besides the ones in BL.

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